Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges (Studies on the Semantic Web) PDF
Preview Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges (Studies on the Semantic Web)
KNOWLEDGE GRAPHS FOR EXPLAINABLE ARTIFICIAL INTELLIGENCE: FOUNDATIONS, APPLICATIONS AND CHALLENGES Studies on the Semantic Web Semantic Web has grown into a mature field of research. Its methods find innovative applica- tions on and off the World Wide Web. Its underlying technologies have significant impact on adjacent fields of research and on industrial applications. This book series reports on the state of the art in foundations, methods, and applications of Semantic Web and its underlying technologies. It is a central forum for the communication of recent developments and com- prises research monographs, textbooks and edited volumes on all topics related to the Se- mantic Web. Editor-in-Chief: Prof. Dr. Pascal Hitzler Department of Computer Science, Kansas State University, Manhattan, KS 66502, USA Email: [email protected] Editorial Board: Diego Calvanese, Vinary Chaudhri, Fabio Ciravegna, Michel Dumontier, Dieter Fensel, Fausto Giunchiglia, Carole Goble, Asunción Gómez Pérez, Frank van Harmelen, Manfred Hauswirth, Ian Horrocks, Krzysztof Janowicz, Michael Kifer, Riichiro Mizoguchi, Mark Musen, Daniel Schwabe, Barry Smith, Steffen Staab, Rudi Studer and Elena Simperl Volume 047 Previously published in this series: Vol. 046 Daniel Dominik Janke, Study on Data Placement Strategies in Distributed RDF Stores Vol. 045 Pavlos Vougiouklis, Neural Generation of Textual Summaries from Knowledge Base Triples Vol. 044 Diego Collarana, Strategies and Techniques for Federated Semantic Knowledge Integration and Retrieval Vol. 043 Filip Ilievski, Identity of Long-Tail Entities in Text Vol. 042 Fariz Darari, Managing and Consuming Completeness Information for RDF Data Sources Vol. 041 Steffen Thoma, Multi-Modal Data Fusion Based on Embeddings Vol. 040 Marilena Daquino, Mining Authoritativeness in Art Historical Photo Archives. Semantic Web Applications for Connoisseurship Vol. 039 Bo Yan, Geographic Knowledge Graph Summarization Vol. 038 Petar Ristoski, Exploiting Semantic Web Knowledge Graphs in Data Mining Vol. 037 Maribel Acosta Deibe, Query Processing over Graph-structured Data on the Web Vol. 036 E. Demidova, A.J. Zaveri, E. Simperl (Eds.), Emerging Topics in Semantic Technologies Vol. 035 Giuseppe Cota, Inference and Learning Systems for Uncertain Relational Data Vol. 034 Ilaria Tiddi, Explaining Data Patterns using Knowledge from the Web of Data Vol. 033 Anne E. Thessen, Application of Semantic Technology in Biodiversity Science Vol. 032 Pascal Hitzler et al. (Eds.), Advances in Ontology Design and Patterns Vol. 031 Michael Färber, Semantic Search for Novel Information Vol. 030 Hassan Saif, Semantic Sentiment Analysis in Social Streams Vol. 029 A. Ławrynowicz, Semantic Data Mining: An Ontology-Based Approach Vol. 028 R. Zese, Probabilistic Semantic Web: Reasoning and Learning Vol. 027 M. Kejriwal, Populating a Linked Data Entity Name System ISSN 1868-1158 (print) ISSN 2215-0870 (online) KNOWLEDGE GRAPHS FOR EXPLAINABLE ARTIFICIAL INTELLIGENCE: FOUNDATIONS, APPLICATIONS AND CHALLENGES Edited by Ilaria Tiddi Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Freddy Lécué Thales, Montreal, Canada & Inria, Sophia Antipolis, France and Pascal Hitzler Kansas State University, Manhattan, Kansas, USA © 2020 Akademische Verlagsgesellschaft AKA GmbH, Berlin All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without prior written permission from the publisher. ISBN 978-3-89838-754-5 (AKA, print) ISBN 978-1-64368-080-4 (IOS Press, print) ISBN 978-1-64368-081-1 (IOS Press, online) doi: 10.3233/SSW47 Bibliographic information available from the Katalog der Deutschen Nationalbibliothek (German National Library Catalogue) at https://www.dnb.de Publisher Akademische Verlagsgesellschaft AKA GmbH, Berlin Represented by Co-Publisher IOS Press IOS Press BV Nieuwe Hemweg 6B 1013 BG Amsterdam The Netherlands Tel: +31 20 688 3355 Fax: +31 20 687 0019 email: [email protected] LEGAL NOTICE The publisher is not responsible for the use which might be made of the following information. PRINTED IN THE NETHERLANDS v Preface Explanations have been the subject of study in a variety of fields for a long time, and are experiencing a new wave of popularity due to the latest advancements in Artificial Intelligence(AI).Whilemachineanddeeplearningsystemsarenowwidelyadoptedfor decision making, they also revealed a major drawback, namely the inability to explain theirdecisionsinawaythathumanscaneasilyunderstandthem.Asaresult,eXplainable AI(XAI)rapidlybecameanactiveareaofresearchinresponsetotheneedofimproving the understandability and trustworthiness of modern AI systems – a crucial aspect for theiradoptionatlargescale,andparticularlyinlife-criticalcontexts. The field of Knowledge Representation and Reasoning (KRR), on the other hand, has a long standing tradition in managing structured knowledge, i.e. modeling, creat- ing,standardising,publishingandsharinginformationinsymbolicform.KRRmethods andtechnologiesdevelopedovertheyearsresultbynowinlargeamountsofstructured knowledge(intheformofontologies,knowledgegraphs,andotherstructuredrepresenta- tions),thatarenotonlymachine-readableandinstandardformats,butalsoopenlyavail- able,andcoveringavarietydomainsatlargescale.Thesestructuredsources,designedto capturecausationasopposedtocorrelationinMachineLearningmethods,couldthere- fore be exploited as sources of background knowledge by eXplainable AI methods in ordertobuildmoreinsightful,trustworthyexplanations. Thisbookprovidestheveryfirstcomprehensivecollectionofresearchcontributions ontheroleofknowledgegraphsforeXplainableAI(KG4XAI).Wegatherstudiesusing KRR as a framework to enable intelligent systems to explain their decisions in a more understandableway,presentingacademicandindustrialresearchfocusedonthetheory, methodsandimplementationsofAIsystemsthatusestructuredknowledgetogenerate reliableexplanations. We include both introductory material on knowledge graphs for readers with only aminimalbackgroundinthefield,andadvancedspecificchaptersdevotedtomethods, applicationsandcase-studiesusingknowledgegraphsasapartofknowledge-based,ex- plainablesystems(KBX-systems).Thefinalchaptersconveycurrentchallengesandfu- turedirectionsofresearchintheareaofknowledgegraphsforeXplainableAI. Ourgoalisnotonlytoprovideascholarly,state-of-the-artoverviewofresearchin thisfield,butalsotofosterthehybridcombinationofsymbolicandandsubsymbolicAI methods,motivatedbythecomplementarystrengthsandlimitationsofboththefieldof KRRandMachineLearning. The editors would like to thank all contributing authors for their efforts in making this bookpossible. March2020 IlariaTiddi FreddyLe´cue´ PascalHitzler This page intentionally left blank vii Contents Preface v Ilaria Tiddi, Freddy Lécué and Pascal Hitzler Part 1. Foundations of Knowledge-Based eXplainable Systems Knowledge Graphs on the Web – An Overview 3 Nicolas Heist, Sven Hertling, Daniel Ringler and Heiko Paulheim Foundations of Explainable Knowledge-Enabled Systems 23 Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne and Deborah L. McGuinness Knowledge Graph Embeddings and Explainable AI 49 Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo Palmonari and Pasquale Minervini Benchmarking the Lifecycle of Knowledge Graphs 73 Michael Röoder, Mohamed Ahmed Sherif, Muhammad Saleem, Felix Conrads and Axel-Cyrille Ngonga Ngomo Part 2. Applications Knowledge-Aware Interpretable Recommender Systems 101 Vito Walter Anelli, Vito Bellini, Tommaso Di Noia and Eugenio Di Sciascio Differentiable Reasoning on Large Knowledge Bases and Natural Language 125 Pasquale Minervini, Matko Bošnjak, Tim Rocktäschel, Sebastian Riedel and Edward Grefenstette Neuro-Symbolic Architectures for Context Understanding 143 Alessandro Oltramari, Jonathan Francis, Cory Henson, Kaixin Ma and Ruwan Wickramarachchi Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning 161 Marjan Alirezaie, Martin Längkvist and Amy Loutfi Knowledge-Based Explanations for Transfer Learning 180 Freddy Lécué, Jiaoyan Chen, Jeff Z. Pan and Huajun Chen Explanations in Predictive Analytics: Case Studies 196 Jiewen Wu, Minh Nguyen, Gia H. Ngo and Nancy F. Chen Generating Explanations in Natural Language from Knowledge Graphs 213 Diego Moussallem, René Speck and Axel-Cyrille Ngonga Ngomo viii Part 3. Challenges for Knowledge-Based eXplainable Systems Directions for Explainable Knowledge-Enabled Systems 245 Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne and Deborah L. McGuinness The Data Ethics Challenges of Explainable AI and Their Knowledge-Based Solutions 262 Mathieu d’Aquin Who Is This Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI 276 Irene Celino Managing Identity in Knowledge-Based Explainable Systems 286 Ilaria Tiddi and Joe Raad Subject Index 301 Author Index 303
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